The Whole is Greater than the Sum of the Parts: Optimizing the Joint Science Return from LSST, Euclid and WFIRST

The focus of this report is on the opportunities enabled by the combination of LSST, Euclid and WFIRST, the optical surveys that will be an essential part of the next decade's astronomy. The sum of these surveys has the potential to be significantly greater than the contributions of the individual parts. As is detailed in this report, the combination of these surveys should give us multi-wavelength high-resolution images of galaxies and broadband data covering much of the stellar energy spectrum. These stellar and galactic data have the potential of yielding new insights into topics ranging from the formation history of the Milky Way to the mass of the neutrino. However, enabling the astronomy community to fully exploit this multi-instrument data set is a challenging technical task: for much of the science, we will need to combine the photometry across multiple wavelengths with varying spectral and spatial resolution. We identify some of the key science enabled by the combined surveys and the key technical challenges in achieving the synergies.Comment: Whitepaper developed at June 2014 U. Penn Workshop; 28 pages, 3 figure

Coded aperture and coded exposure photography : an investigation into applications and methods

This dissertation presents an introduction to the field of computational photography, and provides a survey of recent research. Specific attention is given to coded aperture and coded exposure theory and methods, as these form the basis for the experiments performed

Searching for transits in the Wide Field Camera Transit Survey with difference-imaging light curves

The Wide Field Camera Transit Survey is a pioneer program aiming at for searching extra-solar planets in the near-infrared. The images from the survey are processed by a data reduction pipeline, which uses aperture photometry to construct the light curves. We produce an alternative set of light curves using the difference-imaging method for the most complete field in the survey and carry out a quantitative comparison between the photometric precision achieved with both methods. The results show that differencephotometry light curves present an important improvement for stars with J > 16. We report an implementation on the box-fitting transit detection algorithm, which performs a trapezoid-fit to the folded light curve, providing more accurate results than the boxfitting model. We describe and optimize a set of selection criteria to search for transit candidates, including the V-shape parameter calculated by our detection algorithm. The optimized selection criteria are applied to the aperture photometry and difference-imaging light curves, resulting in the automatic detection of the best 200 transit candidates from a sample of ~475 000 sources. We carry out a detailed analysis in the 18 best detections and classify them as transiting planet and eclipsing binary candidates. We present one planet candidate orbiting a late G-type star. No planet candidate around M-stars has been found, confirming the null detection hypothesis and upper limits on the occurrence rate of short-period giant planets around M-dwarfs presented in a prior study. We extend the search for transiting planets to stars with J ≤ 18, which enables us to set a stricter upper limit of 1.1%. Furthermore, we present the detection of five faint extremely-short period eclipsing binaries and three M-dwarf/M-dwarf binary candidates. The detections demonstrate the benefits of using the difference-imaging light curves, especially when going to fainter magnitudes.Peer reviewe

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Apodized vortex coronagraph designs for segmented aperture telescopes

Current state-of-the-art high contrast imaging instruments take advantage of a number of elegant coronagraph designs to suppress starlight and image nearby faint objects, such as exoplanets and circumstellar disks. The ideal performance and complexity of the optical systems depends strongly on the shape of the telescope aperture. Unfortunately, large primary mirrors tend to be segmented and have various obstructions, which limit the performance of most conventional coronagraph designs. We present a new family of vortex coronagraphs with numerically-optimized gray-scale apodizers that provide the sensitivity needed to directly image faint exoplanets with large, segmented aperture telescopes, including the Thirty Meter Telescope (TMT) as well as potential next-generation space telescopes.Comment: To appear in SPIE proceedings vol. 991